Automated quantitative analysis of biomarker expression in tissue sections or tissue microarrays presents several challenges, including heterogeneity of tissue sections, sub-cellular localization of staining and the presence of background signal. For example, depending on the type of tumor or tissue section being analyzed, the area of interest may represent nearly the entire sample or only a small percentage. For instance, a pancreatic carcinoma or lobular carcinoma of the breast with substantial desmoplastic response may show stromal tissue representing a large percentage of the total area. If the goal of the assay is to determine epithelial cell expression of a given marker, a protocol must be used that evaluates only that region. The protocol must not only be able to select the region of interest but also normalize it, so that the expression level read from any given area can be compared with that of other areas. Sub-cellular localization presents similar challenges. Automated systems and methods for rapidly analyzing tissue sections, including tissue microarrays, which permit the identification and localization of identified biomarkers within sub-cellular compartments in tissues and other cell containing samples, are needed.
Certain methods (including confocal and convolution/deconvolution microscopy) have been used to quantify expression of proteins at the cellular (or sub-cellular) level within a single high power field. These methods, however, are computationally intensive and laborious techniques that operate on multiple serial images. As a result, the current standard for analysis of immunohistology, is conventional pathologist-based analysis and grading of the sample according to scale.
Automated systems for histological analysis of tissue sections often include methods that either have 1) an operator examining an image of a field of view of a stained tissue and adjusting parameters for optimal analysis conditions or 2) consistent settings that treat an entire data set in the same manner, but an operator is still required to make judgment calls in setting the initial parameters i.e. thresholds. Both of these methods suffer at least the disadvantage that the data is not being treated by a single uniform method that is completely objective. These decisions can influence the output of the system and affect data quality. They also add an extra layer of system complexity in that analysis methods can be adjusted to individual experiments or individual specimens and no universal method is used.